Resolving single-cell gene expression by pseudo-temporal integration of transcriptomic and proteomic datasets - Barry, Craig P; Talbo, Gert H; Beauglehole, Aiden; Ovchinnikov, Dmitry; Munro, Trent; Mahler, Stephen; Baker, Kym; Nielsen, Lars K; Mercer, Tim R; Marcellin, Esteban (Molecular & cellular proteomics, 2025)
Single-cell omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell proteomics (scp-MS), offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation profiles remains challenging due to their distinct and complex behaviors. This study presents a novel approach using pseudo-temporal cell ordering to integrate scRNA-seq and scp-MS data, facilitating the analysis of transcription-translation expression dynamics. We collected longitudinal single-cell samples following hypoxia. By leveraging key markers, we constructed pseudo-temporal trajectories for each data type, revealing transcriptional and translational responses to hypoxia. This profile of unified single-cell mRNA and protein expression uncovers distinct regulatory mechanisms, including an immediate transcriptomic response, followed by delayed proteomic expression. It illustrates the use of pseudo-temporal integration to integrate single-cell transcriptomic and proteomic datasets to understand the cellular phenotypes under hypoxic stress and provides a framework for future investigations into transcription-translation dynamics.